Overview

Dataset statistics

Number of variables22
Number of observations597659
Missing cells4895030
Missing cells (%)37.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory139.1 MiB
Average record size in memory244.0 B

Variable types

NUM12
UNSUPPORTED7
CAT3

Warnings

operation_car has constant value "597659" Constant
operation_date has a high cardinality: 19127 distinct values High cardinality
operation_st_esr is highly correlated with destination_esr and 1 other fieldsHigh correlation
destination_esr is highly correlated with operation_st_esr and 1 other fieldsHigh correlation
ssp_station_esr is highly correlated with destination_esr and 1 other fieldsHigh correlation
ssp_station_id is highly correlated with operation_st_idHigh correlation
operation_st_id is highly correlated with ssp_station_idHigh correlation
length has 597659 (100.0%) missing values Missing
adm has 597659 (100.0%) missing values Missing
danger has 597659 (100.0%) missing values Missing
gruz has 597659 (100.0%) missing values Missing
receiver has 597659 (100.0%) missing values Missing
rod_train has 355400 (59.5%) missing values Missing
sender has 597659 (100.0%) missing values Missing
tare_weight has 597659 (100.0%) missing values Missing
weight_brutto has 355394 (59.5%) missing values Missing
df_index has unique values Unique
length is an unsupported type, check if it needs cleaning or further analysis Unsupported
adm is an unsupported type, check if it needs cleaning or further analysis Unsupported
danger is an unsupported type, check if it needs cleaning or further analysis Unsupported
gruz is an unsupported type, check if it needs cleaning or further analysis Unsupported
receiver is an unsupported type, check if it needs cleaning or further analysis Unsupported
sender is an unsupported type, check if it needs cleaning or further analysis Unsupported
tare_weight is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2021-04-14 19:27:11.836499
Analysis finished2021-04-14 19:28:27.661045
Duration1 minute and 15.82 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct597659
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2134349.342
Minimum9
Maximum4189912
Zeros0
Zeros (%)0.0%
Memory size4.6 MiB
2021-04-14T22:28:28.079105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile224820
Q11089142.5
median2135453
Q33197786.5
95-th percentile4004879.5
Maximum4189912
Range4189903
Interquartile range (IQR)2108644

Descriptive statistics

Standard deviation1210381.82
Coefficient of variation (CV)0.5670963964
Kurtosis-1.212187261
Mean2134349.342
Median Absolute Deviation (MAD)1054616
Skewness-0.01428844734
Sum1.275613093e+12
Variance1.465024151e+12
MonotocityStrictly increasing
2021-04-14T22:28:28.231696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
31498261< 0.1%
 
4018091< 0.1%
 
25706741< 0.1%
 
28983381< 0.1%
 
35348011< 0.1%
 
15262041< 0.1%
 
36213091< 0.1%
 
36274541< 0.1%
 
13541761< 0.1%
 
21173801< 0.1%
 
2974121< 0.1%
 
31974421< 0.1%
 
24191441< 0.1%
 
3199451< 0.1%
 
23729641< 0.1%
 
24150541< 0.1%
 
36427931< 0.1%
 
36151521< 0.1%
 
15789451< 0.1%
 
26054851< 0.1%
 
14585891< 0.1%
 
24928201< 0.1%
 
3936211< 0.1%
 
24969181< 0.1%
 
14462951< 0.1%
 
Other values (597634)597634> 99.9%
 
ValueCountFrequency (%) 
91< 0.1%
 
171< 0.1%
 
251< 0.1%
 
261< 0.1%
 
341< 0.1%
 
381< 0.1%
 
431< 0.1%
 
641< 0.1%
 
701< 0.1%
 
731< 0.1%
 
ValueCountFrequency (%) 
41899121< 0.1%
 
41899061< 0.1%
 
41899011< 0.1%
 
41898931< 0.1%
 
41898891< 0.1%
 
41898851< 0.1%
 
41898761< 0.1%
 
41898691< 0.1%
 
41898601< 0.1%
 
41898541< 0.1%
 

index_train
Real number (ℝ≥0)

Distinct25745
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.83944119e+14
Minimum1.04001941e+11
Maximum9.97502819e+14
Zeros0
Zeros (%)0.0%
Memory size4.6 MiB
2021-04-14T22:28:28.426176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.04001941e+11
5-th percentile8.300032558e+14
Q18.634019464e+14
median8.931060029e+14
Q39.37906577e+14
95-th percentile9.74407948e+14
Maximum9.97502819e+14
Range9.973988171e+14
Interquartile range (IQR)7.450463061e+13

Descriptive statistics

Standard deviation1.256464252e+14
Coefficient of variation (CV)0.1421429505
Kurtosis26.61177534
Mean8.83944119e+14
Median Absolute Deviation (MAD)3.399994807e+13
Skewness-4.868512912
Sum5.282971582e+20
Variance1.578702417e+28
MonotocityNot monotonic
2021-04-14T22:28:28.596720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
8.302009469e+14201< 0.1%
 
8.623059489e+14192< 0.1%
 
9.70001942e+14183< 0.1%
 
8.623050029e+14182< 0.1%
 
8.302000199e+14180< 0.1%
 
8.302000039e+14176< 0.1%
 
8.302000049e+14172< 0.1%
 
8.302009429e+14172< 0.1%
 
8.302009499e+14170< 0.1%
 
9.200029309e+14166< 0.1%
 
8.302000129e+14163< 0.1%
 
8.302009499e+14163< 0.1%
 
8.302009449e+14163< 0.1%
 
8.302000419e+14162< 0.1%
 
9.70001943e+14160< 0.1%
 
8.302000029e+14159< 0.1%
 
8.302000329e+14158< 0.1%
 
8.302009499e+14156< 0.1%
 
8.302000029e+14156< 0.1%
 
8.302000069e+14146< 0.1%
 
8.623059509e+14145< 0.1%
 
9.70001945e+14140< 0.1%
 
8.623050049e+14137< 0.1%
 
9.82808121e+14136< 0.1%
 
8.838090309e+14130< 0.1%
 
Other values (25720)59359199.3%
 
ValueCountFrequency (%) 
1.04001941e+111< 0.1%
 
1.049188862e+111< 0.1%
 
1.049208862e+111< 0.1%
 
1.090500295e+1362< 0.1%
 
1.090500694e+1331< 0.1%
 
1.090500794e+1331< 0.1%
 
1.540003995e+1313< 0.1%
 
1.540004597e+131< 0.1%
 
1.540005395e+132< 0.1%
 
1.600989986e+1385< 0.1%
 
ValueCountFrequency (%) 
9.97502819e+141< 0.1%
 
9.97502818e+143< 0.1%
 
9.97502817e+141< 0.1%
 
9.97502816e+149< 0.1%
 
9.97502625e+148< 0.1%
 
9.97502624e+147< 0.1%
 
9.97502622e+144< 0.1%
 
9.97502621e+146< 0.1%
 
9.9750262e+148< 0.1%
 
9.97502619e+142< 0.1%
 

length
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing597659
Missing (%)100.0%
Memory size4.6 MiB

car_number
Real number (ℝ≥0)

Distinct324088
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59794537.31
Minimum20023164
Maximum98099997
Zeros0
Zeros (%)0.0%
Memory size4.6 MiB
2021-04-14T22:28:28.921370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20023164
5-th percentile34107803.4
Q153182486
median58361635
Q363184881
95-th percentile94405771.2
Maximum98099997
Range78076833
Interquartile range (IQR)10002395

Descriptive statistics

Standard deviation14244038.57
Coefficient of variation (CV)0.2382163859
Kurtosis1.543241667
Mean59794537.31
Median Absolute Deviation (MAD)4933874
Skewness0.8113420869
Sum3.573674337e+13
Variance2.028926349e+14
MonotocityNot monotonic
2021-04-14T22:28:29.110896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3782733457< 0.1%
 
4218981152< 0.1%
 
3784676352< 0.1%
 
3782757351< 0.1%
 
4380793251< 0.1%
 
5586482150< 0.1%
 
5582292847< 0.1%
 
5592753747< 0.1%
 
5562642845< 0.1%
 
5586422744< 0.1%
 
5570113043< 0.1%
 
5582294442< 0.1%
 
3202040642< 0.1%
 
5599793637< 0.1%
 
4460666336< 0.1%
 
5592452636< 0.1%
 
5595255036< 0.1%
 
3202025736< 0.1%
 
5586471435< 0.1%
 
3416448335< 0.1%
 
5585181035< 0.1%
 
5570120534< 0.1%
 
5583991434< 0.1%
 
5586486234< 0.1%
 
5591883334< 0.1%
 
Other values (324063)59661499.8%
 
ValueCountFrequency (%) 
200231641< 0.1%
 
210824821< 0.1%
 
210943702< 0.1%
 
211162311< 0.1%
 
211254711< 0.1%
 
211320481< 0.1%
 
211361636< 0.1%
 
211364294< 0.1%
 
211364451< 0.1%
 
211384744< 0.1%
 
ValueCountFrequency (%) 
980999971< 0.1%
 
980999891< 0.1%
 
980999711< 0.1%
 
980999631< 0.1%
 
980999551< 0.1%
 
980999481< 0.1%
 
980999301< 0.1%
 
980999221< 0.1%
 
980999141< 0.1%
 
980999061< 0.1%
 

destination_esr
Real number (ℝ≥0)

HIGH CORRELATION

Distinct712
Distinct (%)0.1%
Missing123
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean918759.5164
Minimum830003
Maximum998100
Zeros0
Zeros (%)0.0%
Memory size4.6 MiB
2021-04-14T22:28:29.304344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum830003
5-th percentile841604
Q1871906
median925701
Q3967808
95-th percentile986103
Maximum998100
Range168097
Interquartile range (IQR)95902

Descriptive statistics

Standard deviation48499.17883
Coefficient of variation (CV)0.05278767508
Kurtosis-1.297601752
Mean918759.5164
Median Absolute Deviation (MAD)44293
Skewness-0.07081747804
Sum5.489918864e+11
Variance2352170347
MonotocityNot monotonic
2021-04-14T22:28:29.485859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
986103375206.3%
 
967808241934.0%
 
864207119852.0%
 
937906112331.9%
 
985702109121.8%
 
932207104871.8%
 
863007100851.7%
 
98470098391.6%
 
83150495511.6%
 
94680193561.6%
 
88790486111.4%
 
88760384761.4%
 
88380980261.3%
 
98020070481.2%
 
98780168231.1%
 
89310666821.1%
 
88140864431.1%
 
92570160341.0%
 
86490259931.0%
 
97000159891.0%
 
86210859821.0%
 
86020657261.0%
 
86220155990.9%
 
97040654730.9%
 
89210354350.9%
 
Other values (687)35403559.2%
 
ValueCountFrequency (%) 
8300038090.1%
 
8301079600.2%
 
8302004470.1%
 
83030410680.2%
 
8307097980.1%
 
83120310670.2%
 
83140024960.4%
 
83150495511.6%
 
831608170< 0.1%
 
831805115< 0.1%
 
ValueCountFrequency (%) 
99810069< 0.1%
 
99750246< 0.1%
 
9974094< 0.1%
 
99710810< 0.1%
 
996904144< 0.1%
 
9968001< 0.1%
 
99660324< 0.1%
 
99630246< 0.1%
 
99580819< 0.1%
 
99550795< 0.1%
 

adm
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing597659
Missing (%)100.0%
Memory size4.6 MiB

danger
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing597659
Missing (%)100.0%
Memory size4.6 MiB

gruz
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing597659
Missing (%)100.0%
Memory size4.6 MiB

loaded
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
1
327021 
2
270638 
ValueCountFrequency (%) 
132702154.7%
 
227063845.3%
 
2021-04-14T22:28:29.663422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-14T22:28:29.759328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:29.861718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
.59765933.3%
 
059765933.3%
 
132702118.2%
 
227063815.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number119531866.7%
 
Other Punctuation59765933.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
059765950.0%
 
132702127.4%
 
227063822.6%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.597659100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1792977100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
.59765933.3%
 
059765933.3%
 
132702118.2%
 
227063815.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1792977100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
.59765933.3%
 
059765933.3%
 
132702118.2%
 
227063815.1%
 

operation_car
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
3
597659 
ValueCountFrequency (%) 
3597659100.0%
 
2021-04-14T22:28:29.986388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-14T22:28:30.068133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:30.147958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
359765933.3%
 
.59765933.3%
 
059765933.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number119531866.7%
 
Other Punctuation59765933.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
359765950.0%
 
059765950.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.597659100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1792977100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
359765933.3%
 
.59765933.3%
 
059765933.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1792977100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
359765933.3%
 
.59765933.3%
 
059765933.3%
 

operation_date
Categorical

HIGH CARDINALITY

Distinct19127
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2020-07-28 18:01:00
 
272
2020-07-28 17:40:00
 
265
2020-07-15 20:00:00
 
261
2020-07-18 09:30:00
 
259
2020-07-20 06:30:00
 
249
Other values (19122)
596353 
ValueCountFrequency (%) 
2020-07-28 18:01:00272< 0.1%
 
2020-07-28 17:40:00265< 0.1%
 
2020-07-15 20:00:00261< 0.1%
 
2020-07-18 09:30:00259< 0.1%
 
2020-07-20 06:30:00249< 0.1%
 
2020-07-18 06:40:00248< 0.1%
 
2020-07-29 18:01:00241< 0.1%
 
2020-07-17 08:00:00240< 0.1%
 
2020-07-17 19:00:00239< 0.1%
 
2020-07-18 19:00:00236< 0.1%
 
2020-07-30 13:41:00235< 0.1%
 
2020-07-26 13:41:00234< 0.1%
 
2020-07-27 14:20:00228< 0.1%
 
2020-07-20 08:02:00224< 0.1%
 
2020-07-23 06:10:00220< 0.1%
 
2020-07-17 05:30:00219< 0.1%
 
2020-07-10 23:30:00216< 0.1%
 
2020-07-17 16:10:00216< 0.1%
 
2020-07-18 12:00:00216< 0.1%
 
2020-07-29 00:20:00216< 0.1%
 
2020-07-30 04:00:00215< 0.1%
 
2020-07-29 20:30:00214< 0.1%
 
2020-07-25 07:15:00213< 0.1%
 
2020-07-20 14:20:00212< 0.1%
 
2020-07-18 18:01:00210< 0.1%
 
Other values (19102)59186199.0%
 
2021-04-14T22:28:30.357361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2329 ?
Unique (%)0.4%
2021-04-14T22:28:30.507020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length19
Min length19

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories4 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0367390732.4%
 
2183872516.2%
 
-119531810.5%
 
:119531810.5%
 
77748366.8%
 
17342546.5%
 
5976595.3%
 
32990612.6%
 
52931832.6%
 
42314812.0%
 
81789611.6%
 
61748291.5%
 
91679891.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number836722673.7%
 
Dash Punctuation119531810.5%
 
Other Punctuation119531810.5%
 
Space Separator5976595.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0367390743.9%
 
2183872522.0%
 
77748369.3%
 
17342548.8%
 
32990613.6%
 
52931833.5%
 
42314812.8%
 
81789612.1%
 
61748292.1%
 
91679892.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1195318100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
597659100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:1195318100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common11355521100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0367390732.4%
 
2183872516.2%
 
-119531810.5%
 
:119531810.5%
 
77748366.8%
 
17342546.5%
 
5976595.3%
 
32990612.6%
 
52931832.6%
 
42314812.0%
 
81789611.6%
 
61748291.5%
 
91679891.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII11355521100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0367390732.4%
 
2183872516.2%
 
-119531810.5%
 
:119531810.5%
 
77748366.8%
 
17342546.5%
 
5976595.3%
 
32990612.6%
 
52931832.6%
 
42314812.0%
 
81789611.6%
 
61748291.5%
 
91679891.5%
 

operation_st_esr
Real number (ℝ≥0)

HIGH CORRELATION

Distinct690
Distinct (%)0.1%
Missing123
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean918726.0711
Minimum830003
Maximum998100
Zeros0
Zeros (%)0.0%
Memory size4.6 MiB
2021-04-14T22:28:30.649601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum830003
5-th percentile841604
Q1871906
median925701
Q3967600
95-th percentile985906
Maximum998100
Range168097
Interquartile range (IQR)95694

Descriptive statistics

Standard deviation48460.41996
Coefficient of variation (CV)0.05274740914
Kurtosis-1.297470179
Mean918726.0711
Median Absolute Deviation (MAD)44293
Skewness-0.07187732784
Sum5.489719016e+11
Variance2348412303
MonotocityNot monotonic
2021-04-14T22:28:30.822140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
985906392616.6%
 
967600258214.3%
 
864207119852.0%
 
937906112331.9%
 
946801112101.9%
 
985609110371.8%
 
980003106761.8%
 
932207104871.8%
 
984502104631.8%
 
863007100851.7%
 
83150495511.6%
 
88790486111.4%
 
88760384761.4%
 
88380980261.3%
 
93690376141.3%
 
98770868401.1%
 
89310666821.1%
 
88140864431.1%
 
92570160341.0%
 
86490259931.0%
 
97000159891.0%
 
86210859821.0%
 
86020657261.0%
 
97040654730.9%
 
89210354350.9%
 
Other values (665)34240357.3%
 
ValueCountFrequency (%) 
8300038090.1%
 
8301079600.2%
 
8302004470.1%
 
83030410680.2%
 
8307097980.1%
 
83120310670.2%
 
83140024960.4%
 
83150495511.6%
 
831608170< 0.1%
 
831805115< 0.1%
 
ValueCountFrequency (%) 
99810069< 0.1%
 
99750246< 0.1%
 
9974094< 0.1%
 
99710810< 0.1%
 
996904144< 0.1%
 
9968001< 0.1%
 
99660324< 0.1%
 
99630246< 0.1%
 
99580819< 0.1%
 
99550795< 0.1%
 

operation_st_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct690
Distinct (%)0.1%
Missing123
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2000531568
Minimum2000035070
Maximum2002025667
Zeros0
Zeros (%)0.0%
Memory size4.6 MiB
2021-04-14T22:28:31.002656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2000035070
5-th percentile2000035530
Q12000036950
median2000038600
Q32001930530
95-th percentile2001933470
Maximum2002025667
Range1990597
Interquartile range (IQR)1893580

Descriptive statistics

Standard deviation831630.3764
Coefficient of variation (CV)0.0004157047007
Kurtosis-0.8133419193
Mean2000531568
Median Absolute Deviation (MAD)1712
Skewness1.089323257
Sum1.195389631e+15
Variance6.91609083e+11
MonotocityNot monotonic
2021-04-14T22:28:31.180177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2000038976392616.6%
 
2000038600258214.3%
 
2001930816119852.0%
 
2000037532112331.9%
 
2000037862112101.9%
 
2000038970110371.8%
 
2000038840106761.8%
 
2000037064104871.8%
 
2000038950104631.8%
 
2001933494100851.7%
 
200193053495511.6%
 
200003556486111.4%
 
200003553084761.4%
 
200003525280261.3%
 
200003749876141.3%
 
200003901668401.1%
 
200003596666821.1%
 
200003519464431.1%
 
200003686860341.0%
 
200003990859931.0%
 
200003862459891.0%
 
200193079459821.0%
 
200193076057261.0%
 
200003863454730.9%
 
200003589054350.9%
 
Other values (665)34240357.3%
 
ValueCountFrequency (%) 
20000350702< 0.1%
 
200003509020< 0.1%
 
20000351103100.1%
 
2000035130111< 0.1%
 
20000351403130.1%
 
20000351624660.1%
 
20000351762< 0.1%
 
20000351825320.1%
 
200003519464431.1%
 
200003521268< 0.1%
 
ValueCountFrequency (%) 
200202566732< 0.1%
 
2002023867134< 0.1%
 
200202350521< 0.1%
 
200202350314< 0.1%
 
20019335386260.1%
 
200193353613880.2%
 
200193353015460.3%
 
200193352230020.5%
 
200193352082< 0.1%
 
200193351820990.4%
 

operation_train
Real number (ℝ≥0)

Distinct5
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10.28559477
Minimum4
Maximum72
Zeros0
Zeros (%)0.0%
Memory size4.6 MiB
2021-04-14T22:28:31.335747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median4
Q34
95-th percentile72
Maximum72
Range68
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.43096262
Coefficient of variation (CV)1.889143316
Kurtosis5.946123616
Mean10.28559477
Median Absolute Deviation (MAD)0
Skewness2.804980394
Sum6147268
Variance377.5623081
MonotocityNot monotonic
2021-04-14T22:28:31.435489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
454014190.4%
 
72519778.7%
 
4455090.9%
 
6429< 0.1%
 
542< 0.1%
 
(Missing)1< 0.1%
 
ValueCountFrequency (%) 
454014190.4%
 
4455090.9%
 
542< 0.1%
 
6429< 0.1%
 
72519778.7%
 
ValueCountFrequency (%) 
72519778.7%
 
6429< 0.1%
 
542< 0.1%
 
4455090.9%
 
454014190.4%
 

receiver
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing597659
Missing (%)100.0%
Memory size4.6 MiB

rodvag
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.93859207
Minimum20
Maximum99
Zeros0
Zeros (%)0.0%
Memory size4.6 MiB
2021-04-14T22:28:31.555138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q160
median60
Q370
95-th percentile96
Maximum99
Range79
Interquartile range (IQR)10

Descriptive statistics

Standard deviation16.68273849
Coefficient of variation (CV)0.2569002184
Kurtosis0.7991182928
Mean64.93859207
Median Absolute Deviation (MAD)0
Skewness0.03813747555
Sum38811134
Variance278.3137635
MonotocityNot monotonic
2021-04-14T22:28:31.688804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
6035296959.1%
 
707372012.3%
 
96513988.6%
 
90499098.4%
 
40355455.9%
 
20211553.5%
 
9347730.8%
 
9543830.7%
 
9219930.3%
 
8717950.3%
 
9919< 0.1%
 
ValueCountFrequency (%) 
20211553.5%
 
40355455.9%
 
6035296959.1%
 
707372012.3%
 
8717950.3%
 
90499098.4%
 
9219930.3%
 
9347730.8%
 
9543830.7%
 
96513988.6%
 
ValueCountFrequency (%) 
9919< 0.1%
 
96513988.6%
 
9543830.7%
 
9347730.8%
 
9219930.3%
 
90499098.4%
 
8717950.3%
 
707372012.3%
 
6035296959.1%
 
40355455.9%
 

rod_train
Real number (ℝ≥0)

MISSING

Distinct22
Distinct (%)< 0.1%
Missing355400
Missing (%)59.5%
Infinite0
Infinite (%)0.0%
Mean38.05333135
Minimum3
Maximum89
Zeros0
Zeros (%)0.0%
Memory size4.6 MiB
2021-04-14T22:28:31.831455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q110
median50
Q352
95-th percentile83
Maximum89
Range86
Interquartile range (IQR)42

Descriptive statistics

Standard deviation23.78135046
Coefficient of variation (CV)0.6249479248
Kurtosis-1.18198489
Mean38.05333135
Median Absolute Deviation (MAD)20
Skewness0.1221588189
Sum9218762
Variance565.5526297
MonotocityNot monotonic
2021-04-14T22:28:31.952581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
108289613.9%
 
50371226.2%
 
52340575.7%
 
40163132.7%
 
63145672.4%
 
83128482.1%
 
3099301.7%
 
2091341.5%
 
5590281.5%
 
7281821.4%
 
5840980.7%
 
8117710.3%
 
8911230.2%
 
565190.1%
 
87166< 0.1%
 
88163< 0.1%
 
57132< 0.1%
 
82121< 0.1%
 
5338< 0.1%
 
329< 0.1%
 
6421< 0.1%
 
661< 0.1%
 
(Missing)35540059.5%
 
ValueCountFrequency (%) 
329< 0.1%
 
108289613.9%
 
2091341.5%
 
3099301.7%
 
40163132.7%
 
50371226.2%
 
52340575.7%
 
5338< 0.1%
 
5590281.5%
 
565190.1%
 
ValueCountFrequency (%) 
8911230.2%
 
88163< 0.1%
 
87166< 0.1%
 
83128482.1%
 
82121< 0.1%
 
8117710.3%
 
7281821.4%
 
661< 0.1%
 
6421< 0.1%
 
63145672.4%
 

sender
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing597659
Missing (%)100.0%
Memory size4.6 MiB

ssp_station_esr
Real number (ℝ≥0)

HIGH CORRELATION

Distinct721
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean918555.0539
Minimum104
Maximum998100
Zeros0
Zeros (%)0.0%
Memory size4.6 MiB
2021-04-14T22:28:32.109162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile841402
Q1872504
median925701
Q3967600
95-th percentile985906
Maximum998100
Range997996
Interquartile range (IQR)95096

Descriptive statistics

Standard deviation50208.59089
Coefficient of variation (CV)0.0546604046
Kurtosis20.86284099
Mean918555.0539
Median Absolute Deviation (MAD)44293
Skewness-1.286791911
Sum5.489826949e+11
Variance2520902599
MonotocityNot monotonic
2021-04-14T22:28:32.492712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
985906392616.6%
 
967600258384.3%
 
864207119862.0%
 
937906113021.9%
 
946801112271.9%
 
985609110371.8%
 
980003108621.8%
 
932207105501.8%
 
984502104631.8%
 
863007100871.7%
 
83150495511.6%
 
88790489681.5%
 
88760384971.4%
 
88380980261.3%
 
93690376411.3%
 
98770868401.1%
 
89310666821.1%
 
97000164601.1%
 
88140864431.1%
 
92570160731.0%
 
86490260421.0%
 
86210860001.0%
 
86020657421.0%
 
89210354750.9%
 
97040654730.9%
 
Other values (696)34113357.1%
 
ValueCountFrequency (%) 
1042< 0.1%
 
7064< 0.1%
 
110720< 0.1%
 
120053< 0.1%
 
180235< 0.1%
 
600017< 0.1%
 
1835021< 0.1%
 
2306001< 0.1%
 
820001186< 0.1%
 
83000310590.2%
 
ValueCountFrequency (%) 
99810069< 0.1%
 
99750255< 0.1%
 
9974094< 0.1%
 
9971081< 0.1%
 
996904140< 0.1%
 
9968002< 0.1%
 
99660323< 0.1%
 
99630250< 0.1%
 
99580819< 0.1%
 
99550795< 0.1%
 

ssp_station_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct711
Distinct (%)0.1%
Missing253
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2000532784
Minimum2000035070
Maximum2002030161
Zeros0
Zeros (%)0.0%
Memory size4.6 MiB
2021-04-14T22:28:32.670582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2000035070
5-th percentile2000035530
Q12000036954
median2000038600
Q32001930530
95-th percentile2001933470
Maximum2002030161
Range1995091
Interquartile range (IQR)1893576

Descriptive statistics

Standard deviation832350.2392
Coefficient of variation (CV)0.0004160642835
Kurtosis-0.8211407721
Mean2000532784
Median Absolute Deviation (MAD)1712
Skewness1.08571745
Sum1.195130288e+15
Variance6.928069207e+11
MonotocityNot monotonic
2021-04-14T22:28:32.838094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2000038976392616.6%
 
2000038600258384.3%
 
2001930816119862.0%
 
2000037532113021.9%
 
2000037862112271.9%
 
2000038970110371.8%
 
2000038840108621.8%
 
2000037064105501.8%
 
2000038950104631.8%
 
2001933494100871.7%
 
200193053495511.6%
 
200003556489681.5%
 
200003553084971.4%
 
200003525280261.3%
 
200003749876411.3%
 
200003901668401.1%
 
200003596666821.1%
 
200003862464601.1%
 
200003519464431.1%
 
200003686860731.0%
 
200003990860421.0%
 
200193079460001.0%
 
200193076057421.0%
 
200003589054750.9%
 
200003863454730.9%
 
Other values (686)34088057.0%
 
ValueCountFrequency (%) 
20000350701< 0.1%
 
200003509020< 0.1%
 
20000351105810.1%
 
2000035130278< 0.1%
 
2000035140200< 0.1%
 
20000351623350.1%
 
20000351762< 0.1%
 
20000351825160.1%
 
200003519464431.1%
 
200003521233< 0.1%
 
ValueCountFrequency (%) 
20020301611< 0.1%
 
20020301594< 0.1%
 
20020301571< 0.1%
 
20020266092< 0.1%
 
20020257571< 0.1%
 
20020256831< 0.1%
 
20020256693< 0.1%
 
200202566732< 0.1%
 
200202566137< 0.1%
 
20020256576< 0.1%
 

tare_weight
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing597659
Missing (%)100.0%
Memory size4.6 MiB

weight_brutto
Real number (ℝ≥0)

MISSING

Distinct4706
Distinct (%)1.9%
Missing355394
Missing (%)59.5%
Infinite0
Infinite (%)0.0%
Mean3116.659988
Minimum0
Maximum8962
Zeros1061
Zeros (%)0.2%
Memory size4.6 MiB
2021-04-14T22:28:33.016650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile433
Q11532
median2399
Q35374
95-th percentile6291
Maximum8962
Range8962
Interquartile range (IQR)3842

Descriptive statistics

Standard deviation2038.150955
Coefficient of variation (CV)0.6539535794
Kurtosis-1.21746278
Mean3116.659988
Median Absolute Deviation (MAD)1324
Skewness0.4401580793
Sum755057632
Variance4154059.315
MonotocityNot monotonic
2021-04-14T22:28:33.158262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
010610.2%
 
62787170.1%
 
16976960.1%
 
62776430.1%
 
16966400.1%
 
11706020.1%
 
63015610.1%
 
16795570.1%
 
17025550.1%
 
16995330.1%
 
16805230.1%
 
62694940.1%
 
62794880.1%
 
62674780.1%
 
62764540.1%
 
62594400.1%
 
17174340.1%
 
62624280.1%
 
16814260.1%
 
62744150.1%
 
63004150.1%
 
62824090.1%
 
16924090.1%
 
17064030.1%
 
10084010.1%
 
Other values (4681)22908338.3%
 
(Missing)35539459.5%
 
ValueCountFrequency (%) 
010610.2%
 
191< 0.1%
 
2190< 0.1%
 
2239< 0.1%
 
2322< 0.1%
 
24157< 0.1%
 
2556< 0.1%
 
2649< 0.1%
 
2724< 0.1%
 
285< 0.1%
 
ValueCountFrequency (%) 
89621< 0.1%
 
767882< 0.1%
 
719838< 0.1%
 
710812< 0.1%
 
706771< 0.1%
 
706671< 0.1%
 
706571< 0.1%
 
706371< 0.1%
 
706271< 0.1%
 
706191< 0.1%
 

Interactions

2021-04-14T22:27:47.048739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:47.302321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:47.555644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:47.804976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:48.033406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:48.269734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:48.497127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:48.719531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:48.957892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:49.193264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:49.433620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:49.723844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:49.951237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:50.179626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:50.408052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:50.654394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:50.914697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:51.161348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:51.396685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:51.669955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:51.911311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:52.150756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:52.459890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:52.715645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:52.932062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:53.167400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:53.399779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:53.685016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:53.974275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:54.229596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:54.477896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:54.724235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:54.972605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:55.205946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:55.447301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:55.712593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:55.935994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:56.163430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:56.400792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:56.649087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:56.902382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:57.143736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:57.386088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:57.613509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:57.854873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:58.077242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:58.323583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:58.590893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:58.824242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:59.050676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:27:59.277032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:28:02.068574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:02.310961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:02.576247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:02.831533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:03.101810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:03.344199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:03.583523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:03.839872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:04.076240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:04.323540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:04.583885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:04.816267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:05.028655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:05.240090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:05.472470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:05.712826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:05.952185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:06.179617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:06.393044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:06.616409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:06.833829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:07.063220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:07.302583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:07.508071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:07.734425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:07.971797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:08.218691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:08.462002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:08.710377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:08.942895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:09.293953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:09.528293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:09.749699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:09.999071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:10.245375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:10.460798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:10.670274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:10.878717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:11.104080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:11.328477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:11.548890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:11.767304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:11.982727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:12.195159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:12.392669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:12.610052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:12.844423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:13.054862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:13.342119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:13.592423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:13.881652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:28:16.204439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:28:16.934523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:28:17.969739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-04-14T22:28:18.475387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:18.728709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:18.989016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:19.230368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:19.452776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:19.666202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:19.889606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:20.127970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:20.351920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:20.563821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:20.910893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:21.131343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:21.333763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:21.550184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:21.783598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-14T22:28:33.717842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-14T22:28:34.104303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-14T22:28:34.474312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-04-14T22:28:22.697854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:24.019562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:26.307249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T22:28:26.943902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
099.100008e+14NaN62845730967808.0NaNNaNNaN1.03.02020-07-16 00:16:00967600.02.000039e+094.0NaN60.010.0NaN967600.02.000039e+09NaN7042.0
1179.700017e+14NaN62845078872701.0NaNNaNNaN2.03.02020-07-15 19:56:00872701.02.001931e+094.0NaN60.0NaNNaN872701.02.001931e+09NaNNaN
2259.171039e+14NaN62847009913206.0NaNNaNNaN2.03.02020-07-16 12:41:00913206.02.000036e+094.0NaN60.0NaNNaN913206.02.000036e+09NaNNaN
3269.700017e+14NaN62847025913206.0NaNNaNNaN2.03.02020-07-16 12:40:00913206.02.000036e+094.0NaN60.0NaNNaN913206.02.000036e+09NaNNaN
4348.902013e+14NaN62846217864300.0NaNNaNNaN2.03.02020-07-16 17:26:00864300.02.001934e+094.0NaN60.0NaNNaN864300.02.001934e+09NaNNaN
5389.700017e+14NaN62846050913206.0NaNNaNNaN2.03.02020-07-16 12:40:00913206.02.000036e+094.0NaN60.0NaNNaN913206.02.000036e+09NaNNaN
6439.379066e+14NaN62843032967808.0NaNNaNNaN1.03.02020-07-16 08:55:00967600.02.000039e+094.0NaN60.058.0NaN967600.02.000039e+09NaN7044.0
7648.626063e+14NaN62844071986103.0NaNNaNNaN1.03.02020-07-15 23:00:00985906.02.000039e+094.0NaN60.0NaNNaN985906.02.000039e+09NaNNaN
8709.676009e+14NaN62843529937906.0NaNNaNNaN2.03.02020-07-16 16:43:00937906.02.000038e+094.0NaN60.0NaNNaN937906.02.000038e+09NaNNaN
9738.621089e+14NaN62841184864601.0NaNNaNNaN1.03.02020-07-16 03:00:00864601.02.001931e+094.0NaN60.0NaNNaN864601.02.001931e+09NaNNaN

Last rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
59764941898548.528019e+14NaN62818117986103.0NaNNaNNaN1.03.02020-07-16 18:00:00985906.02.000039e+094.0NaN60.0NaNNaN985906.02.000039e+09NaNNaN
59765041898608.527081e+14NaN62815881980200.0NaNNaNNaN1.03.02020-07-15 18:30:00980003.02.000039e+094.0NaN60.0NaNNaN980003.02.000039e+09NaNNaN
59765141898698.621081e+14NaN62815279967808.0NaNNaNNaN1.03.02020-07-16 16:08:00967600.02.000039e+094.0NaN60.0NaNNaN967600.02.000039e+09NaNNaN
59765241898768.621081e+14NaN62815170967808.0NaNNaNNaN1.03.02020-07-16 16:08:00967600.02.000039e+094.0NaN60.0NaNNaN967600.02.000039e+09NaNNaN
59765341898858.302009e+14NaN62816137862201.0NaNNaNNaN2.03.02020-07-15 20:00:00862201.02.001931e+094.0NaN60.010.0NaN862201.02.001931e+09NaN1756.0
59765441898898.621081e+14NaN62814181967808.0NaNNaNNaN1.03.02020-07-16 16:08:00967600.02.000039e+094.0NaN60.0NaNNaN967600.02.000039e+09NaNNaN
59765541898939.171039e+14NaN62814041967808.0NaNNaNNaN1.03.02020-07-16 15:13:00967600.02.000039e+094.0NaN60.010.0NaN967600.02.000039e+09NaN5602.0
59765641899018.621081e+14NaN62813555967808.0NaNNaNNaN1.03.02020-07-16 16:08:00967600.02.000039e+094.0NaN60.0NaNNaN967600.02.000039e+09NaNNaN
59765741899068.623051e+14NaN62813316872504.0NaNNaNNaN2.03.02020-07-15 22:20:00872504.02.001931e+0972.0NaN60.0NaNNaN870000.02.001931e+09NaNNaN
59765841899128.302009e+14NaN62827910862201.0NaNNaNNaN2.03.02020-07-15 20:00:00862201.02.001931e+094.0NaN60.010.0NaN862201.02.001931e+09NaN1756.0